Summary
Saudi AZM is hiring an AI Engineer in Riyadh to build, fine-tune, evaluate, and operate production LLM systems, including RAG and multi-agent applications. The role also covers cloud and MLOps infrastructure, Kubernetes, CI/CD and GitOps releases, observability, security controls, and disaster recovery. The engineer will lead and mentor a cloud/AI operations team, set engineering standards across squads, and support vendor and licensing negotiations. Applicants should have a relevant bachelor's degree, 6–8+ years in software, DevOps, or platform engineering, including at least 2 years in applied AI or ML; Arabic and English proficiency is preferred.
The posting
As published on azm.com. Pulled from the employer's site and shown as they wrote it.
Required Qualifications:
Bachelor's degree in Software Engineering, Computer Science, or a related field.
6–8+ years in software, DevOps, or platform engineering, including at least 2 years in an applied AI or ML engineering capacity.
Proven delivery of production AI/LLM systems — not only research or notebook-stage work.
Strong Python; comfortable with Bash and YAML.
Deep hands-on experience with Kubernetes, Docker/Podman, and Terraform.
Production experience with at least one major cloud (Azure preferred; OCI or GCP acceptable).
Demonstrated ownership of CI/CD at scale (Azure DevOps, GitHub Actions) and GitOps release models.
Experience leading a team and setting engineering standards across multiple squads.
Preferred Qualifications:
Master's degree in Applied AI, Machine Learning, or a related discipline.
Fine-tuning experience with QLoRA/LoRA on GPU clusters; PyTorch and Transformers.
Vector database experience (Milvus, Pinecone, or Weaviate) and RAG retrieval design.
Experience delivering on Saudi government or large-scale national digital platforms, with familiarity in local compliance and standards.
Arabic and English professional proficiency
AI systems
Build, fine-tune, and evaluate LLM systems for domain-specific tasks (QLoRA / PEFT on open-weight models such as Llama-3 and Mistral).
Design reproducible evaluation harnesses and A/B test frameworks with tracked metrics: task success rate, safety rate, and latency distributions (p50/p95).
Architect multi-agent and RAG systems (LangGraph, FastAPI, vector databases) from prototype through production.
Implement safety guardrails — input/output validation, allowlist/denylist policies, and controls that reduce invalid or high-risk model actions.
Translate business use cases into deployable prototypes with measurable acceptance criteria, and demo them to stakeholders.
Platform & infrastructure
Design and operate cloud infrastructure and MLOps workspaces (Azure, OCI, or GCP) for AI workloads on Kubernetes and containerized runtimes.
Build CI/CD pipelines and GitOps-based release promotion (Argo CD) across development, test, and production environments.
Implement end-to-end observability (Azure Monitor, Application Insights, ELK) with defined detection and response targets.
Apply network and perimeter security baselines (FW/WAF), automated code quality and SCA scanning (SonarQube, Black Duck), and gated pipelines.
Own disaster recovery design — automated backups, failover, and documented RTO/RPO commitments.
Engineering leadership
Lead and mentor a cloud/AI operations team; define monitoring, incident response, and release governance practices with clear uptime and MTTR targets.
Standardize SDLC practices — branching strategy, PR governance, release management, delivery reporting — to improve lead time and deployment frequency.
Consolidate engineering tooling and workflows; drive migrations and platform standardization where fragmentation slows delivery.
Produce handover documentation and runbooks that make systems auditable and operationally transferable.
Support vendor and licensing negotiations for cloud enterprise agreements